Intrinsic Relationship Research Articles

Fully exploring object relation interaction and hidden state attention for video captioning

Video Captioning (VC) is a challenging task of automatically generating natural language sentences for describing video contents. As a video often contains multiple objects, it is comprehensively crucial to identify multiple objects and model relationships between them. Previous models usually adopt Graph Convolutional Networks (GCN) to infer relational information via object nodes, but there exist uncertainty and over-smoothing issues of relational reasoning. To tackle these issues, we propose a Knowledge Graph based Video Captioning Network (KG-VCN) by fully exploring object relation interaction, hidden state and attention enhancement. In encoding stages, we present a Graph and Convolution Hybrid Encoder (GCHE), which uses an object detector to find visual objects with bounding boxes for Knowledge Graph (KG) and Convolutional Neural Network (CNN). To model intrinsic relations between detected objects, we propose a knowledge graph based Object Relation Graph Interaction (ORGI) module. In ORGI, we design triplets (head, relation, tail) to efficiently mine object relations, and create a global node to enable adequate information flow among all graph nodes for avoiding possibly missed relations. To produce accurate and rich captions, we propose a hidden State and Attention Enhanced Decoder (SAED) by integrating hidden states and dynamically updated attention features. Our SAED accepts both relational and visual features, adopts Long Short-Term Memory (LSTM) to produce hidden states, and dynamically update attention features. Unlike existing methods, we concatenate state and attention features to predict next word sequentially. To demonstrate the effectiveness of our model, we conduct experiments on three well-known datasets (MSVD, MSR-VTT, VaTeX), and our model achieves impressive results significantly outperforming existing state-of-the-art models.

Fundamentals and Challenges of Ligand Modification in Heterogeneous Electrocatalysis.

The development of efficient catalytic materials in the energy field could promote the structural transformation from traditional fossil fuels to sustainable energy. In heterogeneous catalytic reactions, ligand modification is an effective way to regulate both electronic and steric structures of catalytic sites, thus paving a prospective avenue to design the interfacial structures of heterogeneous catalysts for energy conversion. Although great achievements have been obtained for the study and applications of heterogeneous ligand-modified catalysts, the systematical refinements of ligand modification strategies are still lacking. Here, we reviewed the ligand modification strategy from both the mechanistic and applicable scenarios by focusing on heterogeneous electrocatalysis. We elucidated the ligand-modified catalysts in detail from the perspectives of basic concepts, preparation, regulation of physicochemical properties of catalytic sites, and applications in different electrocatalysis. Notably, we bridged the electrocatalytic performance with the electronic/steric effects induced by ligand modification to gain intrinsic structure-performance relations. We also discussed the challenges and future perspectives of ligand modification strategies in heterogeneous catalysis.

The quantum uncertainty relation for skew information of coherence

Abstract The quantum uncertainty relation characterizes the restriction of the quantum coherence on different measurement bases. The skew information of coherence is a coherence measure both operationally and informatically. We study the quantum uncertainty relation for the skew information of coherence under any two orthonormal bases. In qubit and qutrit systems, we derive the quantum uncertainty relations with the lower bounds in the factorized forms by the purity of quantum states and the incompatibility between two orthonormal bases. Especially the lower bound in qubit systems is strictly positive for non-maximally mixed states and incompatible orthonormal bases. These quantum uncertainty relations reveal the intrinsic relation among the coherence, the purity and the incompataibility between two orthonormal bases quantitatively. The advantages of these quantum uncertainty relations are illustrated by some specific examples. This method we developed here is beneficial to some other quantum uncertainty relations.

Optimality of the Howard-Vala T-gate in stabilizer quantum computation

In a remarkable work [Phys. Rev. A 86 022316 (2012)], Howard and Vala introduced a qudit version of the qubit T-gate (i.e., π/8-gate) for any prime dimensional system. This non-Clifford gate is a key ingredient of the paradigm ‘Clifford +T’, which are widely employed in the stabilizer formalism of universal and fault-tolerant quantum computation. Considering the applications and significance of the T-gate, it is desirable to characterize it from various angles. Here we prove that in any prime dimensional system, the Howard-Vala T-gate is optimal, among all diagonal gates, for generating magic resources from stabilizer states when the magic is quantified via the L 1-norm of characteristic functions (Fourier transforms) of quantum states. The quadratic Gaussian sum in number theory plays a key role in establishing this optimality. This highlights an extreme feature of the Howard-Vala T-gate. We further reveal an intrinsic relation between the Howard-Vala T-gate and the Watson-Campbell-Anwar-Browne T-gate [Phys. Rev. A 92 022312 (2015)] for any prime dimensional system.

Computational fluid dynamics investigation of pesticide spraying by agricultural drones

Precise control over pesticide spraying by agricultural drones requires fundamental research of multiphase flow, heat and mass transfer of liquid droplets in downwash flow. In this study, water was used as a carrier agent in pesticide formulations, in which the liquid droplets exhibit a Newtonian fluid behavior. A general computational fluid dynamics (CFD) model that characterizes propeller motion coupled with droplet evaporation and deposition was developed. The downwash flow generated by the propeller rotation was simulated using a multiple reference frame method along with the SST-k-ω turbulence model. Spraying of liquid droplets was simulated using a discrete phase model. The flow model validation was conducted by comparing the simulated velocities with experimental data, and the spray model validation was completed by a comparison of droplet depositions from CFD predictions and experiments. The results indicated that the droplet deposition fluctuates with an increase of propeller speed, and that both temperature and relative humidity affect the droplet deposition. In addition, the relation between the number of particles and the impact velocity was identified quantitatively, impact velocity with respect to particle diameter was predicted, and the effects of surface inclination, surface roughness, and crosswind speed on droplet deposition were analyzed. Moreover, an in-depth discussion about the intrinsic relation between downwash flow and droplet spraying was conducted.

Measuring linear birefringence via rotating-sample transmission Stokes spectropolarimetry

Linear birefringence is a fundamental property of optically anisotropic media, defined by the difference in refractive index experienced by light polarized along orthogonal directions. It is usually manifested in microscopically aligned molecular systems, where a preferential direction of light–matter interaction is created. For instance, the anisotropic structure of calcite crystal causes the famous double-refraction phenomenon. Another common example is commercial adhesive tapes, which are polymeric materials possessing birefringent properties due to their manufacturing processes. The intrinsic relation between birefringence and molecular alignment forges a new analytical route to study materials such as polymeric thin films. Therefore, the capacity of measuring linear birefringence and its fast axis is of paramount importance for the science of anisotropic molecular systems. In this contribution, a comprehensive approach to acquire linear birefringence using rotating-sample transmission Stokes spectropolarimetry is presented and applied to transparent adhesive tapes as a case study. The experimental setup comprises a thermal light source and a spectropolarimeter capable of determining wavelength distributions of Stokes parameters. The samples are carefully aligned in a rotating mount and subjected to a fixed broadband vertically polarized light beam. Then, the transmitted light is analyzed using a rotating retarder type of spectropolarimeter. Through systematic variation of the sample’s angular position, the Stokes parameters of transmitted light are measured for each transmitted wavelength as a function of the sample’s angular position. The linear retardance and fast axis direction relative to the tape’s long axis are then determined from the modulation of Stokes parameters over sample rotation. The model derivation, experimental procedure, and signal processing protocol are described in detail, and the approach is verified with a simple correlation between linear retardance and the number of stacked layers of tape.

Revealing of Intrinsic Relation between Surface Atomic Geometry and Catalytic Mechanism of CuNi Alloy Catalyst in CO2 Hydrogenation to Methanol

Revealing of Intrinsic Relation between Surface Atomic Geometry and Catalytic Mechanism of CuNi Alloy Catalyst in CO₂ Hydrogenation to Methanol

Job Satisfaction and Generational Difference: The Shifting Nature of the Workplace

This study investigates job satisfaction across different generational cohorts within the workforce. It explores how intrinsic rewards, extrinsic rewards, work relations, work-life balance, and employee activation variables influence job satisfaction among these groups. A web-based survey was conducted across the United States, involving 566 participants. Regression analysis was used to identify significant predictors of job satisfaction for each generational cohort. The results revealed that interesting work significantly predicts job satisfaction for all generations, with the strongest impact observed in Generation Z. baby boomers, Generation X, and millennials reported high levels of satisfaction with pay and relations with management. The study highlights distinct generational preferences, contributing to a nuanced understanding of job satisfaction drivers.

Intrinsic relations among chemistry, physics and mathematics in the brazilian high school: a theoretical-methodological approach

This paper reports an investigation into the dialogical relation among significant learning experiences of Chemistry, Physics and Mathematics contents in an interdisciplinary context. It aimed at answering the following questions that are relevant to the field of Exact Sciences: why do students show little interest in these courses?; where is the problem?; did Chemistry and Physics abandon Mathematics when they started to focus more on qualitative and theoretical concepts than on quantitative ones?; and how can the three courses work together to make both teaching and learning processes easier?. This study presented an actual overview of the Brazilian High School by highlighting strengths and weaknesses from the perspective of Chemistry, Physics and Mathematics teaching. The Brazilian educational scenario showed again that the three courses are the ones that students enjoy the least. The research methodology had a qualitative focus since it had an interpretative and descriptive nature. Answers given to the questions may be found in the text intertwined with some worrisome reflections, which also include the triad teacher-student-educational system. Results showed that further studies are needed to find out the reasons for multiple difficulties found in the intrinsic relations among Chemistry, Physics and Mathematics. It is disappointing to know that no satisfactory practical solution has been proposed so far. Everything has been kept in the educational ideal described by research articles, chapters and motivational quotes.

The intrinsic scaling relation between pressure fluctuations and Mach number in compressible turbulent boundary layers

The intrinsic scaling relation between pressure fluctuations and Mach number in compressible turbulent boundary layers

Drug repositioning by collaborative learning based on graph convolutional inductive network

Motivation:Computational drug repositioning is a vital path to improve efficiency of drug discovery, which aims to find potential Drug–Disease Associations (DDAs) to develop new effects of the existing drugs. Many approaches detected novel DDAs from heterogenous network which integrates similar drugs, similar diseases and the known DDAs. However, sparsity of the known DDAs and intrinsic synergic relations on representations of drugs and diseases in the heterogenous network are still the main challenges for DDAs prediction. Results:To address the problems, a novel drug repositioning approach is proposed here. Firstly, a drug similar network is constructed by Gaussian similarity kernel fusion of multisource drug similarities. Likewise, a disease similar network is generated by the same strategy. Secondly, the known DDAs network is extended by a bi-random walk algorithm from the above-mentioned similar networks. Meanwhile, representations of drugs and diseases are learned from their similar networks through graph convolutions and then a DDAs network is induced from the representations. Finally, to discover latent DDAs, the inductive DDAs network is refined iteratively by collaborating with the extended known DDAs network. Comprehensive experimental results show that our method outperforms several state-of-the-art methods for predicting DDAs on indicators including precision, recall, F1-score, MCC, ROC and AUPR. Moreover, case studies suggest that our method is highly effective in practices. The success of our method may be attributed to three aspects: (1) reliable similar relationships of drugs and diseases; (2) enhanced connectivity of the heterogenous network; (3) reasonable collaborative induction on DDAs network. Our method is freely available at https://github.com/BioMLab/DRCLN.

Specification tests for normal/gamma and stable/gamma stochastic frontier models based on empirical transforms

Goodness–of–fit tests for the distribution of the composed error term in a Stochastic Frontier Model (SFM) are suggested. The focus is on the case of a normal/gamma SFM and the heavy–tailed stable/gamma SFM. In the first case the moment generating function is used as tool while in the latter case the characteristic function of the error term is employed. In both cases our test statistics are formulated as weighted integrals of properly standardized data. The new normal/gamma test is consistent, and is shown to have an intrinsic relation to moment–based tests. The finite–sample behavior of resampling versions of both tests is investigated by Monte Carlo simulation, while several real–data applications are also included.

Machine learning modeling of the capacitive performance of N-doped porous biochar electrodes with experimental verification

N-doped porous biochar is considered as a promising carbon material for supercapacitor electrodes application. However, the intrinsic relations and effect mechanisms of the pore structure and N-doping to the capacitive performance are still inscrutable, giving rise to the challenges for enhancing the capacitive performance by regulating the physicochemical properties of N-doped biochar. In this study, various machine learning models were established to predict the specific capacitance of N-doped biochar electrodes based on the pore structure and N-doping properties. The effect mechanisms of pore structure and N-doping to the specific capacitance were also explored. Results showed that Random Forest model predicted the specific capacitance most accurately. The generalization performance of the model was verified to be quite well with our experiments. It is suggested that developing pore structure with abundant micropores plays more important role than N-doping in enhancing the specific capacitance. The optimal interval of each physiochemical property of N-doped biochar were also determined to maximize the specific capacitance. Furthermore, synergistic effects of pore structure and N-doping to the specific capacitance were revealed. This study provides a useful guideline for N-doped porous biochar production with the aim of capacitive performance enhancement.

Hematological toxicity of disinfection by-product iodoacetic acid

To clarify the effect of iodoacetic acid(IAA) on the blood system and electrolyte balance, hence further study the intrinsic relation of blood routine parameters and electrolyte levels, major hematological toxicity effects and their pattern after IAA treatment. Forty-eight 21-day-old male SPF grade Sprague-Dawley(SD) rats were gavaged with 0, 6.25, 12.5 and 25 mg/kg IAA for 31 days. After detections of blood routine and plasma inorganic ion levels, Spearman correlation coefficients were performed to evaluate their relationship. Changes in ferritin, transferrin, hepcidin, C-reactive protein and glyceraldehyde-3-phosphate dehydrogenase(GAPDH) were assessed by enzyme-linked immunosorbent assays. The EDock bioinformatics tool was applied to docking model of IAA and GAPDH. Compared to the control, high-dose IAA exposure had obvious inhibition effect on rat leukocytes with the total number declined by 51.12%, and neutrophils were particularly sensitive to IAA with the number reduced by 73.66%(P<0.01), and rat erythrocytes exhibited a small cell low pigment effect with hemoglobin and hematocrit decreased by 8.60% and 8.70%, respectively(P<0.05). But IAA had little effects on the platelet. Plasma iron, phosphorus, zinc and potassium levels were repressed significantly, while chlorine, sodium and magnesium levels were elevated obviously through IAA exposure. However, plasma calcium levels were hardly affected by IAA. In comparison with the control, iron levels declined by 67.09%, whereas magnesium levels increased by 131.82% in the high-dose group(P<0.01). Overall, correlation analyses uncovered that plasma iron metabolism was most strongly and positively correlated with levels of leukocyte, erythrocyte and platelet system parameters after IAA exposure, and the correlation coefficients of leukocyte number, mean hemoglobin content and mean erythrocyte volume were 0.637, 0.410 and 0.365, respectively(P<0.05). Compared to the control, in the high-dose IAA group, the plasma content of C-reactive protein was significantly upregulated by 13.30%(P<0.05), and plasma levels of transferrin and ferromodulin were also respectively elevated by 12.73% and 11.02%(P<0.05). But plasma levels of ferritin and GAPDH did not differ between groups. The docking model exhibited that IAA could bind to the 150 Cys active site of rat GAPDH did. IAA not only had toxic effects on rat leukocytes and the plasma electrolyte balance, but also generated inflammation and iron deficiency, leading to smaller erythrocytes and lower pigment.

Improving metrology with quantum scrambling in a spin-1 Bose-Einstein condensate coupled to a cavity

Spinor Bose-Einstein condensate is an ideal candidate for implementing the many-body entanglement, quantum measurement and quantum information processing owing to its inherent spin-mixing dynamics. Here we present a system of an 87Rb atomic spin-1 Bose-Einstein condensate coupled to an optical ring cavity, in which cavity-mediated nonlinear interactions give rise to saddle points in the semiclassical phase space, providing a general mechanism for exponential fast scrambling and metrological gain augment. We theoretically study metrological gain and fidelity out-of-time-ordered correlator based on time-reversal protocols and demonstrate that exponential rapid scrambling dynamics can enhance quantum metrology. In addition, we use the out-of-time-ordered correlator to probe dynamical phase transitions. This work is useful to understand the intrinsic relation between the concepts from different subfields of quantum science.

Peculiarities of the intrinsic symmetry of linear and nonlinear optical susceptibility tensors in nonabsorbing inhomogeneous media with nonlocality of optical response

We show that using the Landau–Lifshitz form of the constitutive equations to construct electrodynamics of inhomogeneous nonabsorbing media with nonlocality of optical response leads to previously unknown intrinsic symmetry relations of the spatially dependent tensors of local and nonlocal optical susceptibilities guaranteeing that the energy conservation law is fulfilled. The discovered relations drastically differ from the analogous relations for homogeneous spatially dispersive media and make it possible to correctly write down the balance equation for the energy conservation law in inhomogeneous media with nonlocality of optical response without the previously accepted artificial addition of any new terms to the classical constitutive equations suggested by Landau and Lifshitz.

Culture of Poverty and Sustainable Development: Case of Machar Colony in Karachi

This paper analyzes the intrinsic relation between two key indicators of development; sustainability and poverty. It specifically focuses on culture of poverty and sustainable development. Sustainable development envisions a balanced growth of human societies, meeting the current needs without compromising the required natural resources for future generations. The idea of culture of poverty is put forward in social theory, and the idea revolves around ideals of people experiencing poverty and how it may play an important role in preserving their disadvantaged situation, thus maintaining a cycle of poverty across generations. They may be resistant to considering sustainable choices while taking advantage of economic opportunities. So, the question posed is whether the culture of poverty acts as a hindrance or a facilitator for sustainable development. Given that culture forms an intrinsic part of sustainable development principles, this paper reviews the case of a marginalized low-income community in Karachi, exploring the everyday choices they make within the culture of poverty and the interface they have with the larger framework of sustainable development. Case study methodology has been employed, with qualitative interviews, observation and mapping used as research tools. The results of the research reveal a sense of disempowerment that residents of Machar Colony face, leading them to focus on short-term survival rather than long-term sustainable development objectives. The prevalent culture of poverty impedes progress and development, failing to pave the path for sustainable livelihood.

Signal of phase transition hidden in quasinormal modes of regular AdS black holes

We discuss the intrinsic relations between thermodynamic phase transitions and quasinormal modes in regular AdS black holes, specifically in the Bardeen and Hayward AdS classes. To this end, we calculate the quasinormal modes of massless scalar field perturbations around small and large black holes via the Horowitz-Hubeny method. By investigating the isobaric and isothermal phase transitions for Bardeen and Hayward AdS black holes in detail, we observe that a dramatic change of quasinormal modes appears near the phase transition point of small and large black holes, and that it corresponds to the swallow tail structure in the plane of Gibbs free energy with respect to pressure. Moreover, by analyzing the evolution of black holes along the coexistence curve of small and large black hole phases, we also observe the dramatic change in quasinormal modes. Such a phenomenon confirms the signal of the phase transition in the quasinormal mode spectrum, which can be understood as a thermodynamic signal hidden in the dynamical spectrum.

Joint Anchor Graph Embedding and Discrete Feature Scoring for Unsupervised Feature Selection.

The success of existing unsupervised feature selection (UFS) methods heavily relies on the assumption that the intrinsic relationships among original high-dimensional (HD) data samples exist in the discriminative low-dimension (LD) subspace. However, previous UFS methods commonly construct pairwise graphs and employ l2,1 -norm regularization to severally preserve the local structure and calculate the score of features, which is computationally complex and easy to get stuck into local optimum, so that those approaches cannot be applied in dealing with large-scale datasets in practice. To overcome this challenge, we propose a novel UFS method, in which a novel anchor graph embedding paradigm is designed to extract the local neighborhood relationships among data samples by reducing the computational complexity of graph construction to be linear in the number of data. Moreover, to improve the optimality of selected features as well as the performance of downstream tasks, we propose a discrete feature scoring mechanism, which imposes orthogonal l2,0 -norm constraints on learned projections, in order to enhance the distinction of feature scores as well as reduce the probability of falling into local optimum. In addition, solving the proposed nonconvex and nonsmooth NP-hard problem is challenging, and we present an efficient optimization algorithm to address it and acquire a closed-form solution of the transformation matrix. Extensive experiments demonstrate the effectiveness and efficiency of the proposed UFS by comparison with several state-of-the-art approaches to clustering and image segmentation tasks.

Passive bionic motion of a flexible film in the wake of a circular cylinder: chaos and periodicity, flow–structure interactions and energy evolution

The self-sustained interactions between a flexible film and periodic vortices epitomize the spirit of fish swimming and flag flapping in nature, involving intricate patterns of flow–structure coupling. Here, we comprehensively investigate the multiple coupling states of a film in the cylinder wake mainly with experiments, complemented by theoretical solutions and nonlinear dynamical analyses. Four regimes of film motion states are identified in the parameter space spanned by the reduced velocity and the length ratio. These regimes are (i) keeping stationary, (ii) deflection flutter, (iii) hybrid flutter and (iv) periodic large-amplitude flapping, each governed by a distinct coupling mechanism, involving regular and irregular Kármán vortices, local instability of the elongated shear layers and 2P mode vortex shedding. The film futtering in regimes (ii) and (iii) is substantiated to be chaotic and bears a resemblance to the ‘entraining state’ of fish behind an obstacle in the river. The periodic flapping in regime (iv) manifests itself in an amalgam of standing and travelling waves, and has intrinsic relations to the ‘Kármán gaiting’ of fish in periodic vortices. With the spatiotemporal reconstruction for the periodic flapping, we procure the energy distributions on the film, revealing the energy transfer processes between the film and the large-scale vortices. The findings unequivocally indicate that the flow–structure interaction during the energy-release stage of the film is more intense than that during the energy-extraction stage. Given the similarities, the mathematical and physical methods presented in this work are also applicable to the research on biological undulatory locomotion.